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We live in a complex world that is full of data, and it’s getting even more full every day. In 2020, the world collectively created, captured, copied, and consumed nearly 64.2 zettabytes of data and by 2025 that figure is expected to more than double to 180 zettabytes. Increasingly, companies depend on this data to… Read More »Calling All Data Scientists: Data Observability Needs You
The post Calling All Data Scientists: Data Observability Needs You appeared first on Data Science Central.
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https://thereader.mitpress.mit.edu/the-staggering-ecological-impacts-of-computation-and-the-cloud/
This supports many of the points made in this: https://kv-emptypages.blogspot.com/2021/11/the-carbon-footprint-of-machine-learning.html
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https://github.com/JoaoLages/RATransformers
I have made a package to be able to use pretrained language models on structured data.
By changing self-attention to be relation aware, you are able to pass implicit relations within the input to the model.
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Hey r/ml! I thought people here might enjoy (or possibly have a great discussion about) the latest episode in the MLOps Podcast.
In this episode, I'm speaking with Laszlo Sragner about how data scientists can write better code, how it affects real-world ML projects, and how to build an ML team. We also talk about how to break down ML problems into smaller, more manageable tasks, and a bunch of other things.
You can watch it here: https://www.youtube.com/watch?v=mtwGV-x3nSM
or listen to it here, or read some of the Q&A.
Would love to open up a discussion – what are your best practices for improving code-craft in machine learning projects?
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Now get all official and unofficial code implementations of any AI/ML papers as you're browsing DuckDuckGo, Reddit, Google, Scholar, Arxiv, Twitter and more!
(if code not yet available, request it with 1-click as well!)
https://chrome.google.com/webstore/detail/aiml-papers-with-code-eve/aikkeehnlfpamidigaffhfmgbkdeheil
https://preview.redd.it/zpkx0j1fdwh81.png?width=1265&format=png&auto=webp&s=59f732f9d26223ba20ba4958b389a50617b27f06
https://preview.redd.it/mn656t1fdwh81.png?width=1265&format=png&auto=webp&s=cb6d7d3ddce53cc6c2113e41e80f2d2acd0be964
https://preview.redd.it/0ertru1fdwh81.png?width=1167&format=png&auto=webp&s=a0f5df0905c7dc873ec702c564290cbe9bd7adb7
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This model classifies iris flowers among three species (Setosa, Versicolor or Virginica) based on the length and width measurements of the sepals and petals using Neural Designer
https://www.neuraldesigner.com/learning/examples/iris-flowers-classification
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At the latest UEFA Champions League Finals, one of the world’s most anticipated annual soccer events, pop stars Marshmello, Khalid and Selena Gomez shared the stage for a dazzling opening ceremony at Portugal’s third-largest football stadium — without ever stepping foot in it. The stunning video performance took place in a digital twin of the Read article >
The post Peak Performance: Production Studio Sets the Stage for Virtual Opening Ceremony at European Football Championship appeared first on The Official NVIDIA Blog.
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A new deep-learning algorithm trained to optimize doses of propofol to maintain unconsciousness during general anesthesia could augment patient monitoring.
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Internet of things (IoT) holds an enormous promise in making urban transport systems smarter in terms of safety, energy-saving, ecologically favorable, and efficiency. The efficiency of optimizing transportation in real-time is the key pillar of successful deployment of IoT. Ecosystem to Develop Pivoted on Expanding Use Cases Several developed nations notably Singapore, the U.S., and… Read More »IoT in Intelligent Transportation Systems Anchors Smart Traffic for Smart Cities
The post IoT in Intelligent Transportation Systems Anchors Smart Traffic for Smart Cities appeared first on Data Science Central.
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Amazon SageMaker Autopilot makes it possible for organizations to quickly build and deploy an end-to-end machine learning (ML) model and inference pipeline with just a few lines of code or even without any code at all with Amazon SageMaker Studio. Autopilot offloads the heavy lifting of configuring infrastructure and the time it takes to build […]
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Architecture evaluation is a systematic approach for identifying flaws and dangers in architectural designs. The evaluation process is ideally performed before they are implemented.
Typically, neural architecture search (NAS) systems are used for architectural evaluation. Neural architecture search (NAS) is an AutoML branch that aims to find the best deep-learning model architecture for a task. The systems achieve this by finding an architecture that will achieve the best performance metric on the given task dataset and search space of possible architectures. However, this usually necessitates training each proposed model completely on the dataset, which takes a long time. Continue Reading
Paper: http://proceedings.mlr.press/v139/xu21m/xu21m.pdf
Github: https://github.com/Jingjing-NLP/KNAS
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Datacenter accelerators are pieces of hardware that are specifically built to process visual data. It’s a physical device or software program that boosts a computer’s overall performance. Continuous advancements in creating and delivering data center (DC) machine learning (ML) accelerators, such as TPUs and GPUs, have proven crucial for scaling up contemporary ML models and applications. These upgraded accelerators’ ultimate performance (e.g., FLOPs) is orders of magnitude higher than that of standard computing systems.
However, there is a rapidly widening gap between the potential peak performance supplied by state-of-the-art hardware and the actual achievable performance when ML models run on these kinds of hardware. Continue Reading
Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Li\_Searching\_for\_Fast\_Model\_Families\_on\_Datacenter\_Accelerators\_CVPR\_2021\_paper.pdf
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Looking for a challenge? Try maneuvering a Kenyan minibus through traffic or dropping seed balls on deforested landscapes. Or download Africa’s Legends and battle through fiendishly difficult puzzles with Ghana’s Ananse or Nigeria’s Oya by your side. Games like these are connecting with a hyper-connected African youth population that’s growing fast. Africa is the youngest Read article >
The post New Levels Unlocked: Africa’s Game Developers Reach Toward the Next Generation appeared first on The Official NVIDIA Blog.
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Hey all!
We're building towards a GPT3 level moment in computer vision, and here's our v0 - https://youtu.be/P7zcc8iZ0YA
This v0 runs on 13B parameters, with 18B and 34B model iterations coming in the pipeline.
Access to the model is gated as of now to help us monitor scale, you can sign up at - https://banana-dev.typeform.com/carrot
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